RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving

Related tags

Deep LearningRTS3D
Overview

RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving (AAAI2021).

RTS3D is efficiency and accuracy stereo 3D object detection method for autonomous driving.

RTS3D

Introduction

RTS3D is the first true real-time system (FPS>24) for stereo image 3D detection meanwhile achieves 10% improvement in average precision comparing with the previous state-of-the-art method. RTS3D only require RGB images without synthetic data, instance segmentation, CAD model, or depth generator.

Highlights

  • Fast: 33 FPS of single image test speed in KITTI benchmark with 384*1280 resolution
  • Accuracy: SOTA on the KITTI benchmark.
  • Anchor Free: No 2D or 3D anchor are reauired
  • Easy to deploy: RTS3D uses conventional convolution operations and MLP, so it is very easy to deploy and accelerate.

RTS3D Baseline and Model Zoo

All experiments are tested with Ubuntu 16.04, Pytorch 1.0.0, CUDA 9.0, Python 3.6, single NVIDIA 2080Ti

IoU Setting 1: Car IoU > 0.5, Pedestrian IoU > 0.25, Cyclist IoU > 0.25

IoU Setting 2: Car IoU > 0.7, Pedestrian IoU > 0.5, Cyclist IoU > 0.5

  • Training on KITTI train split and evaluation on val split.
Class Iteration FPS AP BEV IoU Setting1 AP 3D IoU Setting1 AP BEV IoU Setting2 AP 3D IoU Setting2
- - - Easy / Moderate / Hard Easy / Moderate / Hard Easy / Moderate / Hard Easy / Moderate / Hard
Car- Recall-11 1 90.9 89.83, 77.05, 68.28 89.27, 70.12, 61.17 73.20, 53.62, 46.44 60.87, 42.38, 36.44
Car- Recall-40 1 90.9 92.92, 76.17, 66.62 90.35, 71.37, 63.52 78.12, 54.75, 47.09 60.34, 39.32, 32.97
Car- Recall-11 2 45.5 90.41, 78.70, 70.03 90.26, 77.23, 68.28 76.56, 56.46, 48.20 63.65, 44.50, 37.48
Car- Recall-40 2 45.5 95.75, 79.61, 69.69 93.57, 76.64, 66.72 78.12, 54.75, 47.09 63.99, 41.78, 34.96
  • Training on KITTI train split and evaluation on val split.
    • FCE Space Resolution: 10 * 10 * 10
    • Recall split: 11
    • Iteration: 2
    • Model: (Google Drive), (Baidu Cloud 提取码:4t4u)
Class AP BEV IoU Setting1 AP 3D IoU Setting1 AP BEV IoU Setting2 AP 3D IoU Setting2
- Easy / Moderate / Hard Easy / Moderate / Hard Easy / Moderate / Hard Easy / Moderate / Hard
Car 90.18, 78.46, 69.76 89.88, 76.64, 67.86 74.95, 54.07, 46.78 58.50, 39.74, 34.83
Pedestrian 57.12, 48.82, 40.88 56.36, 48.29, 40.22 32.16, 26.31, 21.28 26.95, 20.77, 19.74
Cyclist 54.48, 35.78, 30.80 53.86, 30.90, 30.52 33.59, 20.80, 20.14 31.05, 20.26, 18.93

Installation

Please refer to INSTALL.md

Dataset preparation

Please download the official KITTI 3D object detection dataset and organize the downloaded files as follows:

KM3DNet
├── kitti_format
│   ├── data
│   │   ├── kitti
│   │   |   ├── annotations
│   │   │   ├── calib /000000.txt .....
│   │   │   ├── image(left[0-7480] right[7481-14961] input augmentatiom)
│   │   │   ├── label /000000.txt .....
|   |   |   ├── train.txt val.txt trainval.txt
│   │   │   ├── mono_results /000000.txt .....
├── src
├── demo_kitti_format
├── readme
├── requirements.txt

Getting Started

Please refer to GETTING_STARTED.md to learn more usage about this project.

Acknowledgement

License

RTS3D is released under the MIT License (refer to the LICENSE file for details). Portions of the code are borrowed from, CenterNet, iou3d and kitti_eval (KITTI dataset evaluation). Please refer to the original License of these projects (See NOTICE).

Citation

If you find this project useful for your research, please use the following BibTeX entry.

@misc{2012.15072,
Author = {Peixuan Li, Shun Su, Huaici Zhao},
Title = {RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving},
Year = {2020},
Eprint = {arXiv:2012.15072},
}
🎃 Core identification module of AI powerful point reading system platform.

ppReader-Kernel Intro Core identification module of AI powerful point reading system platform. Usage 硬件: Windows10、GPU:nvdia GTX 1060 、普通RBG相机 软件: con

CrashKing 1 Jan 11, 2022
Must-read Papers on Physics-Informed Neural Networks.

PINNpapers Contributed by IDRL lab. Introduction Physics-Informed Neural Network (PINN) has achieved great success in scientific computing since 2017.

IDRL 330 Jan 07, 2023
A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python

deepface Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid

Sefik Ilkin Serengil 5.2k Jan 02, 2023
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

Bayesian Methods for Hackers Using Python and PyMC The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chap

Cameron Davidson-Pilon 25.1k Jan 02, 2023
A curated list of long-tailed recognition resources.

Awesome Long-tailed Recognition A curated list of long-tailed recognition and related resources. Please feel free to pull requests or open an issue to

Zhiwei ZHANG 542 Jan 01, 2023
The description of FMFCC-A (audio track of FMFCC) dataset and Challenge resluts.

FMFCC-A This project is the description of FMFCC-A (audio track of FMFCC) dataset and Challenge resluts. The FMFCC-A dataset is shared through BaiduCl

18 Dec 24, 2022
Code and data for "TURL: Table Understanding through Representation Learning"

TURL This Repo contains code and data for "TURL: Table Understanding through Representation Learning". Environment and Setup Data Pretraining Finetuni

SunLab-OSU 63 Nov 23, 2022
OOD Generalization and Detection (ACL 2020)

Pretrained Transformers Improve Out-of-Distribution Robustness How does pretraining affect out-of-distribution robustness? We create an OOD benchmark

littleRound 57 Jan 09, 2023
Author's PyTorch implementation of Randomized Ensembled Double Q-Learning (REDQ) algorithm.

REDQ source code Author's PyTorch implementation of Randomized Ensembled Double Q-Learning (REDQ) algorithm. Paper link: https://arxiv.org/abs/2101.05

109 Dec 16, 2022
Code for our paper "Multi-scale Guided Attention for Medical Image Segmentation"

Medical Image Segmentation with Guided Attention This repository contains the code of our paper: "'Multi-scale self-guided attention for medical image

Ashish Sinha 394 Dec 28, 2022
P-Tuning v2: Prompt Tuning Can Be Comparable to Finetuning Universally Across Scales and Tasks

P-tuning v2 P-Tuning v2: Prompt Tuning Can Be Comparable to Finetuning Universally Across Scales and Tasks An optimized prompt tuning strategy achievi

THUDM 540 Dec 30, 2022
Build Low Code Automated Tensorflow, What-IF explainable models in just 3 lines of code.

Build Low Code Automated Tensorflow explainable models in just 3 lines of code.

Hasan Rafiq 170 Dec 26, 2022
SurfEmb (CVPR 2022) - SurfEmb: Dense and Continuous Correspondence Distributions

SurfEmb SurfEmb: Dense and Continuous Correspondence Distributions for Object Pose Estimation with Learnt Surface Embeddings Rasmus Laurvig Haugard, A

Rasmus Haugaard 56 Nov 19, 2022
Apply Graph Self-Supervised Learning methods to graph-level task(TUDataset, MolculeNet Datset)

Graphlevel-SSL Overview Apply Graph Self-Supervised Learning methods to graph-level task(TUDataset, MolculeNet Dataset). It is unified framework to co

JunSeok 8 Oct 15, 2021
Python scripts for performing stereo depth estimation using the MobileStereoNet model in ONNX

ONNX-MobileStereoNet Python scripts for performing stereo depth estimation using the MobileStereoNet model in ONNX Stereo depth estimation on the cone

Ibai Gorordo 23 Nov 29, 2022
Localizing Visual Sounds the Hard Way

Localizing-Visual-Sounds-the-Hard-Way Code and Dataset for "Localizing Visual Sounds the Hard Way". The repo contains code and our pre-trained model.

Honglie Chen 58 Dec 07, 2022
Vision-Language Transformer and Query Generation for Referring Segmentation (ICCV 2021)

Vision-Language Transformer and Query Generation for Referring Segmentation Please consider citing our paper in your publications if the project helps

Henghui Ding 143 Dec 23, 2022
PyTorch code accompanying our paper on Maximum Entropy Generators for Energy-Based Models

Maximum Entropy Generators for Energy-Based Models All experiments have tensorboard visualizations for samples / density / train curves etc. To run th

Rithesh Kumar 135 Oct 27, 2022
Improving 3D Object Detection with Channel-wise Transformer

"Improving 3D Object Detection with Channel-wise Transformer" Thanks for the OpenPCDet, this implementation of the CT3D is mainly based on the pcdet v

Hualian Sheng 107 Dec 20, 2022
PyTorch implementation of D2C: Diffuison-Decoding Models for Few-shot Conditional Generation.

D2C: Diffuison-Decoding Models for Few-shot Conditional Generation Project | Paper PyTorch implementation of D2C: Diffuison-Decoding Models for Few-sh

Jiaming Song 90 Dec 27, 2022